Category: BI

Business Intelligence & Metadata Tutorial:

Business Intelligence is a terminology refers to taking advantage of data and converting them into an intelligent information or knowledge by carefully observing data patterns or trends. These findings are key factors in helping any business to improve it’s current business processes to gain more on customer satisfaction, increase sales, produce more profit etc. The knowledge observed from several report based analysis may lead to new business changes or improvements thus helping the organization to grow in the targeted direction. Browse through the various topics listed below to know more.

OLAP Database – Multidimensional:

This is a type of database that is optimized for data warehouse, data mart and online analytical processing (OLAP) applications. The main advantage of this database is query performance.

Relational databases make it easy to work with individual records, whereas multidimensional databases are designed for analyzing large groups of records. Relational database is typically accessed using a Structured Query Language (SQL) query. A multidimensional database allows a user to ask questions like “How many mortgages have been sold in New Jersey city” and “How many credit cards have been purchased in a particular county?”.

OLAP & its Hybrids:

OLAP, an acronym for Online Analytical Processing is an approach that helps organization to take advantages of DATA. Popular OLAP tools are Cognos, Business Objects, Micro Strategy etc. OLAP cubes provide the insight into data and helps the topmost executives of an organization to take decisions in an efficient manner.

Technically, OLAP cube allows one to analyze data across multiple dimensions by providing multidimensional view of aggregated, grouped data. With OLAP reports, the major categories like fiscal periods, sales region, products, employee, promotion related to the product can be ANALYZED very efficiently, effectively and responsively. OLAP applications include sales and customer analysis, budgeting, marketing analysis, production analysis, profitability analysis and forecasting etc.

OLAP Analysis:

Imagine an organization that manufactures and sells goods in several States of USA which employs hundreds of employees in its manufacturing, sales and marketing division etc. In order to manufacture and sell this product in profitable manner, the executives need to analyse (OLAP analysis) the data on the product and think about various possibilities and causes for a particular event like loss in sales, less productivity or increase in sales over a particular period of the year.

During the OLAP analysis, the top executives may seek answers for the following:

1. Number of products manufactured.

2. Number of products manufactured in a location.

3. Number of products manufactured on time basis within a location.

4. Number of products manufactured in the current year when compared to the previous year.

5. Sales Dollar value for a particular product.

6. Sales Dollar value for a product in a location.

7. Sales Dollar value for a product in a year within a location.

8. Sales Dollar value for a product in a year within a location sold or serviced by an employee.

OLAP tools help executives in finding out the answers, not only to the above mentioned measures, even for the very complex queries by allowing them to slice and dice, drill down from higher level to lower level summarized data, rank, sort, etc.

Example of OLAP Analysis Report:

Time Dimension Id

Location Dimension Id

Product Dimension Id

Organization Dimension Id

Sales Dollar

DateTimeStamp

1

1

100001

1

1000

1/1/2005 11:23:31 AM

3

1

100001

1

750

1/1/2005 11:23:31 AM

1

1

100001

2

1000

1/1/2005 11:23:31 AM

3

1

100001

2

750

1/1/2005 11:23:31 AM

In the above example of OLAP analysis, data can be sliced and diced, drilled up and drilled down for various hierarchies like time dimension, location dimension, product dimension, and organization dimension. This would provide the topmost executives to take a decision about the product performance in a location/time/organization. In OLAP reports, Trend analysis can be also made by comparing the sales value of a particular product over several years or quarters.

Business Intelligence Tools:

Business Intelligence Tools help to gather, store, access and analyze corporate data to aid in decision-making. Generally these systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation,
product profitability, statistical analysis, inventory and distribution analysis.

With Business Intelligence Tools, various data like customer related, product related, sales related, time related, location related, employee related etc. are gathered and analysed based on which important strategies or rules are formed and goals to achieve their target are set. These decisions are very efficient and effective in promoting an Organisation’s growth.

Since the collected data can be sliced across almost all the dimensions like time,location, product, promotion etc., valuable statistics like sales profit in one region for the current year can be calculated and compared with the previous year statistics.

Business Intelligence:

Business Intelligence is a technology based on customer and profit oriented models that reduces operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision making capabilities at no time. Business Intelligence Models are based on multi dimensional analysis and key performance indicators (KPI) of an enterprise.

Business Intelligence applications that are based on Business Intelligence Models are created by Business Intelligence software which provides the aggregated details about suppliers, customers, internal activities, business to business transactions to the managers or whoever needs it to take better corporate decisions.

Many business questions or situations need to be analyzed in order to achieve the target of an enterprise with the help of several managers or executives in each cadre. Below are some of the samples of these questions.

» Business Intelligence in Finance:

What is the net income, expenses, gross profit, and net profit for this quarter/year?

» Business Intelligence in Accounts:

What is the sales amount this month and what is the outstanding pending payment?

» Business Intelligence in Purchase:

Who is the vendor to be contacted to purchase products?

» Business Intelligence in Production:

How many products are manufactured in each production unit today/weekly/monthly?

» Business Intelligence in Sales:

How many products have been sold in each area today/weekly/monthly?

» Business Intelligence in Quality:

How many products have been defective today/weekly/monthly/quarterly/yearly?

» Business Intelligence in Service:

Are the customers satisfied with the quality?

These business intelligence questions are related with why, what, how, when, and business intelligence reports (olap reports) are useful in providing solutions to the above questions by means of reporting, score cards, balance score cards that are helpful in managerial decisions.

Metadata Reports:

Metadata stored in a repository can be produced in the form of reports for easy understanding and these reports are very useful in explaining about the various objects or data structures and the relationship between these objects. The following products like Schema Logic Enterprise Suite, Rochade, Metatrieve, Datamapper, Metacenter, Metadata Integration Frame work stores and handles metadata in an efficient and effective manner.

Following are few examples for metadata reports.

A metadata report on business process activities, data flow.

A metadata report on source and target columns can be generated to find out all the source and target field names, data types, definitions etc.

metadata report on workflow can be generated to find out all the workflows, workflow properties, workflow scheduling, workflow execution details etc.

A metadata report on mapping can be generated to find out all the sources and targets in the mapping, source fields and target fields participated in the mapping etc.

A metadata report on business intelligence can be generated to find out all the score cards and balanced score cards.

Role of Metadata in ETL:

When you deal with a data warehouse, various phases like Business Process Modeling, Data Modeling, ETL, Reporting etc., are inter-related with each other and they do contain their own metadata. For example in ETL, it will be very difficult for one to extract, transform and load source data into a data warehouse, if there is no metadata available for the source like where and how to get the source data.

Let us explain the role of metadata in the ETL process with the help of an example table shown below which contains information about an organisation’s employees.

Employee Name

Employee Age

Employee Salary

Employee Title

John Hick

36

$3000

Informatica Specialist

In the above table, the second row, containing information like John Hick, 36, $3000, Informatica Specialist are known as Data. Whereas the first row, (i.e) table header containing headings like Employee Name, Employee Age, Employee Salary, Employee Title are called as Metadata for the above said data.

An organization may be using data modeling tools, such as Erwin, Embarcadero, Oracle designer, Sybase Power Designer etc., for developing data models. Functional and technical team should have spent much time and effort in creating the data model’s data structures (tables, columns, data types, procedures, functions, triggers etc.,). By using metadata capturing tools, these data structures can be imported into metadata repository which we call it as metadata.

For example when you work with Informatica’s Metadata Exchange, it captures the metadata present in these tools and loads into the repository. There is no need for Informatica developer to create these data structures once again since metadata(data definitions) have been already captured and stored. Similarly most of the ETL tools have that capability to capture metadata from RDBMS, files, ERP, Applications etc.

In ETL, Metadata Repository is where all the metadata information about source, target, transformations, mapping, workflows, sessions etc., are stored.
From this repository, metadata can be manipulated, queried and retrieved with the help of wizards provided by metadata capturing tools. During the ETL process, when we are mapping source and target systems, we are actually mapping their metadata.

In any organization, a useful metadata often stored in a repository can be a handy resource to know about the organization’s information systems. Assume that each department in an organization may have a different business definitions, data types, attribute names for the same attribute or they may have a single business definition for many attributes. These anomalies can be overcome by properly maintaining metadata for these attributes in the centralized repository.

Thus metadata plays a vital role in explaining about how, why, where data can be found, retrieved, stored and used efficiently in an information management system.

Technical Metadata Example:

Metadata:

Metadata is data about data. Metadata comes into picture when we need to know about how data is stored and where it is stored. Metadata tools are helpful in capturing business metadata and the following section explains business metadata.

Business Metadata:

In IT, Business Metadata is adding additional text or statement around a particular word that adds value to data. Business Metadata is about creating definitions, business rules. For example, when tables and columns are created the following business metadata would be more useful for generating reports to functional and technical team.

The advantage is of this business metadata is whether they are technical or non-technical, everybody would understand what is going on within the organization.

Table’s Metadata: While creating a table, metadata for definition of a table, source system name, source entity names, business rules to transform the source table, and the usage of the table in reports should be added in order to make them available for taking metadata reports.

Column’s Metadata: Similarly for columns, source column name (mapping), business rules to transform the source column name, and the usage of the column in reports should be added for taking metadata reports.